Paper Detail
Tianyu Lin, Jooyoung Ryu, Puvada Sreevarsha, Rahul Srinivasaragavan, Riya Satavlekar, Susan Kim, Nidhi Soley, Yujie Yan, Ishan Vatsaraj, Carl Harris, Aimon Rahman, Vishal Patel, Joseph Greenstein, Casey Taylor, Kemar E. Green
Eye movements, including saccades, are widely regarded as highly sensitive and objective biomarkers of neurophysiologic states. Detecting saccadic signatures in neurologic diseases offers a rapid, portable alternative to brain imaging, avoiding access and cost barriers. Currently, there are no robust AI-enabled video-oculographic solutions (e.g., digital biomarkers) for screening, triaging, or localizing brain abnormalities due to privacy issues and scarce datasets. In this work, we propose the first fully synthetic, patient-free, multimodal eye movement generation pipeline for generalizable saccade analysis. Using this synthetic dataset, we trained a deep learning classifier to distinguish between normal and abnormal (hypometria and hypermetria) saccadic accuracies and evaluated its performance on real-world clinical data. The model achieved an AUROC of 0.76 and a sensitivity of 0.71, showing that the synthetic data has strong potential to generalize for clinical applications, including as a screening tool in at-home and emergency room settings or a tool for precise neuroanatomic localization.
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@article{lin2026geneyepose,
title = {GenEyePose: Patient-Free, Knowledge-Based Saccadic Eye Movement Modeling for Digital Neurophysiologic Biomarker Development},
author = {Tianyu Lin and Jooyoung Ryu and Puvada Sreevarsha and Rahul Srinivasaragavan and Riya Satavlekar and Susan Kim and Nidhi Soley and Yujie Yan and Ishan Vatsaraj and Carl Harris and Aimon Rahman and Vishal Patel and Joseph Greenstein and Casey Taylor and Kemar E. Green},
year = {2026},
abstract = {Eye movements, including saccades, are widely regarded as highly sensitive and objective biomarkers of neurophysiologic states. Detecting saccadic signatures in neurologic diseases offers a rapid, portable alternative to brain imaging, avoiding access and cost barriers. Currently, there are no robust AI-enabled video-oculographic solutions (e.g., digital biomarkers) for screening, triaging, or localizing brain abnormalities due to privacy issues and scarce datasets. In this work, we propose the },
url = {https://arxiv.org/abs/2606.09681},
keywords = {cs.CV},
eprint = {2606.09681},
archiveprefix = {arXiv},
}
{}